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Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Mohamed Amine Ferrag, Othmane Friha, Leandros Maglaras, Helge Janicke, Lei Shu
2021 IEEE Access  
the importance of using federated deep learning by intrusion detection systems and malware detection systems as a decentralized machine learning approach for detecting cyber security attacks in IoT networks  ...  FEDERATED LEARNING-BASED CYBER SECURITY INTRUSION DETECTION Tab. 4 presents the federated learning-based systems for intrusion and malware detection in IoT applications. A.  ...  ., and Habilitation degrees in computer science from Badji Mokhtar-Annaba University, Annaba, Algeria, in June, 2008, June, 2010, June, 2014, and April, 2019, respectively.  ... 
doi:10.1109/access.2021.3118642 fatcat:222fgsvt3nh6zcgm5qt4kxe7c4

Table of Contents

2021 IEEE Transactions on Industrial Informatics  
Leung 7607 Evolutionary Deep Belief Network for Cyber-Attack Detection in Industrial Automation and Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Picone 7791 SPECIAL SECTION ON CLOUD-EDGE COMPUTING FOR CYBER-PHYSICAL SYSTEMS AND INTERNET-OF-THINGS Detection and Location of Aged Cable Segment in Underground Power Distribution System Using Deep  ... 
doi:10.1109/tii.2021.3097924 fatcat:ctptdnq6kvdjdjaxyuukbm4gnm

2021 Index IEEE Transactions on Industrial Informatics Vol. 17

2021 IEEE Transactions on Industrial Informatics  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Note that the item title is found only under the primary entry in the Author Index.  ...  ., +, TII Feb. 2021 860-870 DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber-Physical Systems.  ... 
doi:10.1109/tii.2021.3138206 fatcat:ulsazxgmpfdmlivigjqgyl7zre

Table of Contents

2022 IEEE Transactions on Industrial Informatics  
Mao 4275 A Deep One-Class Intrusion Detection Scheme in Software-Defined Industrial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Dev 4215 Juggler-ResNet: A Flexible and High-Speed ResNet Optimization Method for Intrusion Detection System in Software-Defined Industrial Networks . . . . . . . . . . Z. Zhu, W. Zhai, H. Liu, J.  ... 
doi:10.1109/tii.2022.3148848 fatcat:j26zd4gqa5gi3kcpojca6mrzpm

Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning

Mohamed Amine Ferrag, Othmane Friha, Djallel Hamouda, Leandros Maglaras, Helge Janicke
2022 IEEE Access  
In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems  ...  machine learning as well as deep learning) in both centralized and federated learning modes.  ...  machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning.  ... 
doi:10.1109/access.2022.3165809 fatcat:k6qpodd7hnbddjjdlxzkktjs74

Table of Contents

2022 IEEE Transactions on Industrial Informatics  
Hu 3193 Adaptive Resilient Control of Cyber-Physical Systems Under Actuator and Sensor Attacks . . . . . . . . . . . . . . . . . . .  ...  Operating State Reconstruction in Cyber Physical Smart Grid for Automatic Attack Filtering . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tii.2022.3143757 fatcat:qg2ncfi7njh57bsutl2qmdkf5u

Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics [article]

Petar Radanliev, David De Roure, Kevin Page, Max Van Kleek, Rafael Mantilla Montalvo, Omar Santos, La Treall Maddox, Stacy Cannady, Pete Burnap, Eirini Anthi, Carsten Maple
2020 arXiv   pre-print
This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for  ...  predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing.  ...  Deep Neural Network (DNN) has been applied with distributed deep learning to collect network-based and host-based intrusion detection systems (NBID and HBID) [40] .  ... 
arXiv:2005.12150v1 fatcat:2aexajsa3ze2fbnskqry7sayd4

Deep Learning Approaches for Intrusion Detection in IIoT Networks – Opportunities and Future Directions

Thavavel Vaiyapuri, Zohra Sbai, Haya Alaskar, Nourah Ali
2021 International Journal of Advanced Computer Science and Applications  
In this paper, we present a survey of deep learning-based IDS technique for IIoT.  ...  The main objective of this research is to provide the various deep learning-based IDS detection methods, datasets and comwparative analysis.  ...  ACKNOWLEDGMENT The authors are very grateful to thank their Deanship of Scientific Research for technical and financial support in publishing this work successfully.  ... 
doi:10.14569/ijacsa.2021.0120411 fatcat:vhguxgz3x5fxvde65ljlvmhanq

Cybersecurity of Critical Infrastructures: Challenges and Solutions

Leandros Maglaras, Helge Janicke, Mohamed Amine Ferrag
2022 Sensors  
The authors in propose a novel machine learning solution for threat detection in a smart city [5] .The proposed hybrid Deep learning model that consists of QRNN and CNN improves cyber threat analysis  ...  The major target of cyber attacks can be a country's Critical National Infrastructures (CNIs) like ports, hospitals, water, gas or electricity producers, that use and rely on Industrial Control Systems  ... 
doi:10.3390/s22145105 pmid:35890784 pmcid:PMC9317681 fatcat:ww5uhdj5y5cbxcdvxezwe37cau

2021 Index IEEE Transactions on Sustainable Computing Vol. 6

2022 IEEE Transactions on Sustainable Computing  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Note that the item title is found only under the primary entry in the Author Index.  ...  -Industrial control March 2021 131-143 Assessing the Effectiveness of Attack Detection at a Hackfest on Industrial Forensics Control Systems.  ... 
doi:10.1109/tsusc.2021.3136425 fatcat:jpk2jcwejbevfkq6cvgcpylie4

Blockchain-based Collaborated Federated Learning for Improved Security, Privacy and Reliability [article]

Amir Afaq, Zeeshan Ahmed, Noman Haider, Muhammad Imran
2022 arXiv   pre-print
prone to cyber-attacks.  ...  Federated Learning (FL) provides privacy preservation by allowing the model training at edge devices without the need of sending the data from edge to a centralized server.  ...  Based on our research, we have identified that blockchain can complement the performance advantages of collaborated federated learning and could open new research directions for the future.  ... 
arXiv:2201.08551v1 fatcat:urdgdb2nerhxhjoynrkhnryqem

The future of Artificial Intelligence in Cybersecurity: A Comprehensive Survey

Feng Tao, Muhammad Akhtar, Zhang Jiayuan
2021 EAI Endorsed Transactions on Creative Technologies  
The increase in the incidence and quality of cyber-attacks is driving AI-enabled cyber systems.  ...  AI in Cybersecurity Market scheme helps organizations in observance, detecting, reporting, and countering cyber threats to keep up information confidentiality.  ...  In order to compensate for cyber-attacks, a smart classical control system is built.  ... 
doi:10.4108/eai.7-7-2021.170285 fatcat:fvzohjzivzf3rgvfgtfwbm4qly

Guest Editorial: Special Issue on Internet of Things for Industrial Security for Smart Cities

Huimin Lu, Pin-Han Ho, Mohsen Guizani
2021 IEEE Internet of Things Journal  
federated learning-based deep anomaly detection framework for sensing time-series data in IIoT.  ...  In the article "Deep anomaly detection for time-series data in Industrial IoT: A communication-efficient on-device federated learning approach," Liu et al. proposed a new communication-efficient on-device  ... 
doi:10.1109/jiot.2021.3067077 fatcat:cvctxdsr7vcwzctlcpi7puweay

A Brief Review on Internet of Things, Industry 4.0 and Cybersecurity

Roman Rudenko, Ivan Miguel Pires, Paula Oliveira, João Barroso, Arsénio Reis
2022 Electronics  
These devices are data sources that provide abundant information on manufacturing processes in an industrial environment.  ...  The advance of industrialization regarding the optimization of production to obtain greater productivity and consequently generate more profits has led to the emergence of Industry 4.0, which aims to create  ...  [26] aimed to demonstrate a deep learning model based on forensics with the primary objective of identifying intrusions and cyber-attacks in the IIoT network, giving the name Deep IFS to this system  ... 
doi:10.3390/electronics11111742 fatcat:rsnuf76gtfbpvejavve5vp2zly

Relational Characteristics of Maliciousness and Hacker in a Cyberattack

2019 International journal of recent technology and engineering  
Therefore, artificial intelligence-based learning algorithms are emerging to effectively respond to cybersecurity threats.  ...  In this study, however, we briefly focused on the factors that affect these attacks.  ...  ACKNOWLEDGMENT This paper is sponsored of project funding of Industrial Academia Cooperation in Baekseok University.  ... 
doi:10.35940/ijrte.b1092.0982s1019 fatcat:2dnhiluwojgbdb5m32jwfgit7u
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